The term “model complexity” may refer to two different meanings in deep learning.
First, model complexity may refer to capacity of deep models in expressing or approximating complicated distribution functions [1]}.
Second, it may describe how complicated the distribution functions are with some parameterized deep models [2]}. These two meanings are captured by the notions of model expressive capacity and model effective complexity, respectively.
